Oral communication device and computing systems for processing data and outputting oral feedback, and related methods

ABSTRACT

Typical graphical user interfaces and predefined data fields limit the interaction between a person and a computing system. An oral communication device and a data enablement platform are provided for ingesting oral conversational data from people, and using machine learning to provide intelligence. At the front end, an oral conversational bot, or chatbot, interacts with a user. On the backend, the data enablement platform has a computing architecture that ingests data from various external data sources as well as data from internal applications and databases. These data and algorithms are applied to surface new data, identify trends, provide recommendations, infer new understanding, predict actions and events, and automatically act on this computed information. The chatbot then provides audio data that reflects the information computed by the data enablement platform. The system and the devices, for example, are adaptable to various industries.

CROSS-REFERENCE TO RELATED APPLICATION

This patent application claims priority to U.S. Provisional PatentApplication No. 62/543,777, filed on Aug. 10, 2017, and titled “OralCommunication Device and Computing Architecture For Processing Data andOutputting User Feedback, and Related Methods”, the entire contents ofwhich are hereby incorporated by reference.

TECHNICAL FIELD

In one aspect, the following generally relates to an oral communicationdevice and related computing architectures and methods for processingdata and outputting user feedback, such as via audio or visual media, orboth. In another aspect, the following generally relates to computingarchitectures and machine intelligence to ingest large volumes of datafrom many different data sources, and to output actionable data.

DESCRIPTION OF THE RELATED ART

In recent years computing technology has been developed to provideusers, with computer devices, data that is actionable. Differentcomputing architectures and software programs have been developed toingest data and process the same. Many of the existing computingarchitectures are suitable for processing data from internal databases.Furthermore, the computer networks are more conventionally designed,where multiple user devices (e.g. desktop computers) access a centralserver or a cloud server over the Internet to input and retrieve data.However, it is herein recognized that these computing architectures andsoftware programs are not suitable for ingesting the growing velocity,volume and variety of data. In particular, the proliferation ofdifferent types of electronic devices (e.g. machine-to-machinecommunication, user-oriented devices, Internet of Things devices, etc.)has increased the volume and the variety of data to be analyzed andprocessed.

Furthermore, users typically interact with their user devices to studythe data using a keyboard and a mouse or trackpad, along with a displaydevice (e.g. a computer monitor). With the growing popularity of tabletsand mobile devices (e.g. smart phones), applications or “apps” have beendeveloped that allow a user to see the data on built-in touchscreens.Graphical user interfaces (GUIs), such as in Customer RelationsManagement (CRM) software, has many input forms, tables, charts andgraphs in order to visually organize the data. However, it is hereinrecognized that these types of computing device interactions are stillcomplex, difficult and time consuming for a user. Furthermore, the inputforms (e.g. data fields, data types, data entries, etc.) are typicallypredetermined by design and, therefore, limit the type of data beinginputted.

These, and other technical challenges, lead to more limited outputteddata and more limited automated machine actions.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments will now be described by way of example only with referenceto the appended drawings wherein:

FIG. 1 is a schematic diagram of an example computing architecture foringesting user data via user devices, and providing big datacomputations and machine learning using a data enablement platform.

FIG. 2 is another schematic diagram, show another representation of thecomputing architecture in FIG. 1.

FIG. 3 is a schematic diagram of oral communication devices (OCDs) incommunication with respective user devices, which are in turn incommunication with the data enablement platform.

FIG. 4A is a schematic diagram showing an OCD being used in a meetingand showing the data connections between various devices and the dataenablement platform.

FIG. 4B is a schematic diagram showing different embodiments of an OCD,including wearable devices, and an OCD embodiment configured to provideaugmented reality or virtual reality.

FIG. 5 is a block diagram showing example components of the OCD.

FIG. 6 is a schematic diagram showing an example computing architecturefor an artificial intelligence (AI) platform, which is part of the dataenablement platform.

FIG. 7 is a schematic diagram showing another example aspect of thecomputing architecture for the AI platform.

FIG. 8 is a schematic diagram showing an example computing architecturefor an extreme data platform, which is an example aspect of the AIplatform.

FIG. 9 is a flow diagram of executable instructions for processing voicedata using a user device and further processing the data using the dataenablement platform.

FIG. 10 is a block diagram of example software modules residing on theuser device and the data enablement platform, which are used in thesales and marketing industry.

FIG. 11 is an example schematic diagram showing the flow of data betweenthe software modules shown in FIG. 10.

FIGS. 12-19 are screenshots of example graphical user interfaces (GUIs)relating to the software modules shown in FIG. 10.

FIG. 20 is a flow diagram of example executable instructions for usingthe data enablement platform to monitor a given company.

FIG. 21 is a flow diagram of example executable instructions for usingthe data enablement platform to monitor a given company, including usingboth internal and external data.

FIG. 22 is a flow diagram of example executable instructions for usingthe data enablement platform to identify one or more contacts peoplerelevant to given company or person.

FIG. 23 is a flow diagram of example executable instructions for usingthe data enablement platform to provide business analytics.

FIG. 24 is a flow diagram of example executable instructions for usingthe data enablement platform to modify the audio parameters of certainphrases and sentences.

FIG. 25 is a flow diagram of example executable instructions for usingthe data enablement platform to extract data features from voice dataand associated background noise.

FIG. 26 is an example embodiment of a Digital Signal Processing(DSP)-based voice synthesizer.

FIG. 27 is an example embodiment of a hardware system used by theDSP-based voice synthesizer.

FIG. 28 is a flow diagram of example executable instructions forbuilding a voice library of a given person.

FIG. 29 is a flow diagram of example executable instructions for a smartdevice interacting with a user.

FIG. 30 is a flow diagram of example executable instructions for a smartdevice interacting with a user.

FIG. 31 is a flow diagram of example executable instructions for a smartdevice interacting with a user.

FIG. 32 is a flow diagram of example executable instructions for a smartdevice interacting with a user, which continues from the flow diagram inFIG. 31.

FIG. 33 is a flow diagram of example executable instructions for a smartdevice interacting with a user in relation to a given topic and using asynthesized voice of a given person.

FIG. 34 is a flow diagram of example executable instructions for a smartdevice interacting with a user in relation to a given conversation modeand using a synthesized voice of a given person.

DETAILED DESCRIPTION

It will be appreciated that for simplicity and clarity of illustration,where considered appropriate, reference numerals may be repeated amongthe figures to indicate corresponding or analogous elements. Inaddition, numerous specific details are set forth in order to provide athorough understanding of the example embodiments described herein.However, it will be understood by those of ordinary skill in the artthat the example embodiments described herein may be practiced withoutthese specific details. In other instances, well-known methods,procedures and components have not been described in detail so as not toobscure the example embodiments described herein. Also, the descriptionis not to be considered as limiting the scope of the example embodimentsdescribed herein.

It is herein recognized that typical computing architectures andsoftware programs, such as for CRMs, are limited to ingest limited typesof data. These types of data are based on internal databases. However,it is herein recognized that there are many more types of data, and fromdifferent data sources, that can be used and processed to provideactionable data to a person or to machines to initiate automaticactions. For example, it is recognized that data sources can include,but are not limited to, any one or more of: data from Internet of Things(IoT) devices, CRM software, social data networks and related platforms,internal databases, data obtained via individual user devices, stockexchange platforms, news servers, blogs, third-party search engines,etc. From these example sources, it is appreciated that the types ofdata are varied and that the data can be constantly updating.

For example, it is herein recognized that a sales team is investigatinga potential sale of product or services, which involve certain things(e.g. the product itself, supporting equipment that relates to one ormore of the manufacture, storage or delivery of the product, supportingequipment or things that relate to the provision of the service). It isrecognized that current CRM technology does not track these certainthings in real-time. Nor does CRM technology track these certain thingswith high detail and accuracy, which would inform the capability and theparameters of the potential sale of the product or services.

Furthermore, it is herein recognized that in many data assistivecomputing systems, such as for CRM technology, the data inputs includepredefined fields. A person typically uses a keyboard or a touchscreendevice to input text into the predefined fields of a GUI. For example,companies such as SalesForce, Microsoft, and SugarCRM, to name a few,provide technology systems and software that are predominantlycompliance driven systems and that do not encourage nor provide theright information at the right time for sales people when a newopportunity presents itself. These predefined input forms and input GUIsare processed using more typical computing software. It is hereinrecognized that such an approach inherently ignores utilizing thevariety and the volume of data that is available for various datasources, which likely have data types and data formats that do notconform to the predefined input forms and input GUIs.

It is herein recognized that people often think, talk and act innon-predefined patterns. In other words, the thought process or aconversation between people does not typically follow predefined GUIsand predefined input forms. Using existing CRM software, a person, suchas a sales associate, will need to extract their notes from aconversation and input the extracted portions of information into thepredefined GUIs and input forms. This process is even more burdensomeand complex when many people have a meeting, and a person must identifythe relevant information to type into a predefined GUI or predefinedinput forms. Not only is this data entry process inefficient, but thetechnology inherently ignores other data from the individual's thoughts,or the conversations, or the meetings, or combinations thereof.

Furthermore, typical CRM GUIs attempt to display actionable data usingvarious charts, diagrams, and graphs. However, this technical approachto conveying data can be overwhelming to a user's cognitive abilities.Furthermore, a user, such as a sales representative, may be multitasking(e.g. driving, trying to read other information, etc.) while trying tounderstand the charts, diagrams, graphs, and text, which may makeunderstanding the information presented in the CRM GUI's even moredifficult.

Furthermore, it herein recognized that existing CRM technologies lacksuser devices and related computing architecture that can process oraldata from a user, and interact with the user using one or more of audioand visual feedback. It is recognized these above technical challengesand others, in turn, lead to other difficulties and limitations in thecontext of CRM technologies.

It will be appreciated that while many of the examples relate to CRMtechnology and sales, the technologies described herein are applicableto other data enabling systems, also called data assistive decisionmaking systems. For example, the devices, computing architectures andcomputational functionality described herein could be assistive in amilitary environment, a security environment, a political environment, amedical operations environment, a company operations environment, aneducation environment, etc.

Sales people, sales management, and senior executives continue to wastea lot of time understanding, qualifying, evaluating, and predictingsales opportunities, even when using existing CRM technologies. Forexample, the wasted time areas, aforementioned, begins when a newopportunity presents itself to the sales person and the amount of wastedtime continues to aggregate and increase as the opportunity moves fromone sales process step to the next.

Examples of wasted time to understand, qualify, evaluate and predictsales opportunities include but are not limited to:

-   -   Salesperson working on an opportunity that is already known as        not a good fit for the product or service that the company        offers, but the salesperson should pursue other leads;    -   Salesperson is researching the opportunity organization and        doesn't realize the executive champion or the buyer has left the        company;    -   Salesperson is spending more time researching about the target        opportunity organization, the industry, the people involved with        the opportunity and not enough time in front of face to face        leads;    -   Salesperson forced to enter opportunities into a CRM system with        little to no confidence in the information being entered into        the CRM system, but required for compliance purposes;    -   Salespersons' manager evaluating all of the low to no confidence        opportunities in his or her sales team and wasting time        attempting to evaluate and gain confidence on these low        confidence opportunities for compliance purposes; and    -   President and CFO's evaluating low to no confidence        opportunities with the intent to predict revenue knowing the        pipeline revenue data is inaccurate, that a revenue shortfall is        expected, and that unexpected revenue gaps will arise.

The above technical limitations of current CRM technologies can alsofurther cause any one or more of the following: unexpected projectedrevenue gaps and blips; last minute forecast revenue projectioncorrections; inability to forecast revenue with little to no confidence;late sales opportunity pipeline discrepancies and corrections for theentire sales organization; late sales opportunity pipeline discrepanciesand corrections for sales groups; late sales opportunity pipelinediscrepancies and corrections for individual salespeople; inability toprovide timely feedback to specific sales people with sales strategy andtactic recommendations; and inability to provide timely feedback fromsales to marketing with specific successful and unsuccessful marketingcampaign information that turn into successful or unsuccessful salesleads/early stage opportunities.

Therefore, one or more user devices, computing architecture andcomputing functionality are described herein to address one or more ofthe above technical challenges.

In an example embodiment, an oral communication user device (e.g. adevice that includes a microphone, or some other sensor that recordsuser's language input) records oral information from a user (e.g. theuser's word and sounds) to interact with a data enablement system. Thedata enablement system processes the voice data to extract, at least thewords and of the spoken language, and accordingly processes the datausing artificial intelligence computing software and data sciencealgorithms. The data obtained from the oral communication device isprocessed in combination with, or comparison with, or both, internaldata specific to an organization (e.g. a given company) and externaldata (e.g. available from data sources outside a given company). Thecomputing architecture ingests data from external data sources andinternal data sources to provide real-time outputs or near real-timedata outputs, or both. The data outputs are presented to the user asaudio feedback, or visual feedback, or both. Other types of userfeedback may be used, including tactile feedback. Other machine actionsmay be initiated or executed based on the data outputs.

In an example embodiment, the devices, systems and the methods describedherein provide salespeople, sales managers, and executives with moreintelligent, timely, and predictable sales opportunity and revenueinformation while encouraging and helping sales people to activelyengage with opportunities prior to entering information into traditionalCRM systems.

Turning to FIG. 1, a user device 102 interacts with a user 101. The userdevice 102 includes, amongst other things, input devices 113 and outputdevices 114. The input devices include, for example, a microphone andkeyboard (e.g. physical keyboard or touchscreen keyboard, or both). Theoutput devices include, for example, an audio speaker and a displayscreen. Non-limiting examples of user devices include a mobile phone, asmart phone, a tablet, a desktop computer, a laptop, an e-book, anin-car computer interface, wearable devices, augmented reality devices,and virtual reality devices. The user device is in communication with a3^(rd) party cloud computing service 103, which typically includes banksof server machines. Multiple user devices 111, which correspond tomultiple users 112, can communicate with the 3^(rd) part cloud computingservice 103.

The cloud computing service 103 is in data communication with one ormore data science server machines 104. These one or more data scienceserver machines are in communication with internal application anddatabases 105, which can reside on separate server machines, or, inanother example embodiment, on the data science server machines. In anexample embodiment, the data science computations executed by the datascience servers and the internal applications and the internal databasesare considered proprietary to given organization or company, andtherefore are protected by a firewall 106. Currently known firewallhardware and software systems, as well as future known firewall systemscan be used.

The data science server machines, also called data science servers, 104are in communication with an artificial intelligence (AI) platform 107.The AI platform 107 includes one or more AI application programminginterfaces (APIs) 108 and an AI extreme data (XD) platform 109. As willbe discussed later, the AI platform runs different types of machinelearning algorithms suited for different functions, and these algorithmscan be utilized and accessed by the data science servers 104 via an AIAPI.

The AI platform also is connected to various data sources 110, which maybe 3^(rd) party data sources or internal data sources, or both.Non-limiting examples of these various data sources include: newsservers, stock exchange servers, IoT data, enterprise databases, socialmedia data, etc. In an example embodiment, the AI XD platform 109ingests and processes the different types of data from the various datasources.

In an example embodiment, the network of the servers 103, 104, 105, 107and optionally 110 make up a data enablement system. The data enablementsystem provides relevant to data to the user devices, amongst otherthings. In an example embodiment, all of the servers 103, 104, 105 and107 reside on cloud servers.

An example of operations is provided with respect to FIG. 1, using thealphabetic references. At operation A, the user device 102 receivesinput from the user 101. For example, the user is speaking and the userdevice records the audio data (e.g. voice data) from the user. The usercould be recording or memorializing thoughts to himself or herself, orproviding himself or herself a to-do list to complete in the future, orproviding a command or a query to the data enablement system. In anexample embodiment, a data enablement application is activated on theuser device and this application is placed into a certain mode, eitherby the user or autonomously according to certain conditions.

At operation B, the user device transmits the recorded audio data to the3^(rd) party cloud computing servers 103. In an example embodiment, theuser device also transmits other data to the servers 103, such ascontextual data (e.g. time that the message was recorded, informationabout the user, the mode of the data enablement application during whichthe message was recorded, etc.). Non-limiting examples of modes of thedata enablement application include: to-do list mode; opportunitiesmode; introductions mode; meeting notes mode; calendar mode; news mode;and other functional modes for different user applications. Theseservers 103 apply machine intelligence, including artificialintelligence, to extract data features from the audio data. These datafeatures include, amongst other things: text, sentiment, emotion,background noise, a command or query, or metadata regarding the storageor usage, or both, of the recorded data, or combinations thereof.

At operation C, the servers 103 send the extracted data features and thecontextual data to the data science servers 104. In an exampleembodiment, the servers 103 also send the original recorded audio datato the data science servers 104 for additional processing.

At operation D, the data science servers 104 interact with the internalapplications and databases 105 to process the received data. Inparticular, the data science servers store and executed one or morevarious data science algorithms to process the received data (fromoperation C), which may include processing proprietary data andalgorithms obtained from the internal applications and the databases105.

In alternative, or in addition to operation D, the data science servers104 interact with the AI platform 107 at operations E and G. In anexample embodiment, the data science servers 104 have algorithms thatprocess the received data, and these algorithms transmit information tothe AI platform for processing (e.g. operation E). The informationtransmitted to the AI platform can include: a portion or all of the datareceived by the data science servers at operation C; data obtained frominternal applications and databases at operation D; results obtained bythe data science servers from processing the received data at operationC, or processing the received data at operation D, or both; or acombination thereof. In turn, the AI platform 107 processes the datareceived at operation E, which includes processing the informationingested from various data sources 110 at operation F. Subsequently, theAI platform 107 returns the results of its AI processing to the datascience servers in operation G.

Based on the results received by the data science servers 104 atoperation G, the data science servers 104, for example, updates itsinternal applications and databases 105 (operation D) or its own memoryand data science algorithms, or both. The data science servers 104 alsoprovide an output of information to the 3^(rd) party cloud computingservers 104 at operation H. This outputted information may be a directreply to a query initiated by the user at operation A. In anotherexample, either in alternative or in addition, this outputtedinformation may include ancillary information that is eitherintentionally or unintentionally requested based on the received audioinformation at operation A. In another example, either in alternative orin addition, this outputted information includes one or more commandsthat are either intentionally or unintentionally initiated by receivedaudio information at operation A. These one or more commands, forexample, affect the operation or the function of the user device 102, orother user devices 111, or IoT devices in communication with the 3^(rd)party cloud computing servers 104, or a combination thereof.

In an example embodiment at operation H and I, the text data along withthe current mode of the data enablement application is sent to the userdevice 102, and the user device 102 locally uses a synthesized voicelibrary and the text to generate and output spoken audio data atoperation J. After the user device receives the text data, the userdevice and the current mode, the user device propagates this text datato other modes of the data enablement application, even though they arenot being currently activated.

In an alternative example embodiment, the 3^(rd) party cloud computingservers 104 take the data received at operation H and appliestransformation to the data, so that the transformed data is suitable foroutput at the user device 102. For example, the servers 104 receive textdata at operation H, and then the servers 104 transform the text data tospoken audio data. This spoken audio data is transmitted to the userdevice 102 at operation I, and the user device 102 then plays or outputsthe audio data to the user at operation J.

This process is repeated for various other users 112 and their userdevices 111. For example, another user speaks into another user deviceat operation K, and this audio data is passed into the data enablementplatform at operation L. The audio data is processed, and audio responsedata is received by the another user device at operation M. This audioresponse data is played or outputted by the another user device atoperation N.

In some other example embodiments, the user uses one or more oftouchscreen gestures, augmented reality gestures or movements,neuromuscular gestures, brain signal inputs, virtual reality gestures ormovements, typing, etc. to provide inputs into the user device 102 atoperation A, either in addition or in alternative to the oral input. Inanother example embodiment, the user device 102 provides visualinformation (e.g. text, video, pictures) either in addition or inalternative to the audio feedback at operation J.

Turning to FIG. 2, another example of the servers and the devices areshown in a different data networking configuration. The user device 102,the cloud computing servers 103, the data science servers 104, AIcomputing platform 107, and the various data sources 110 are able totransmit and receive data via a network 201, such as the Internet. In anexample embodiment, the data science servers 104 and the internalapplications and databases 105 are in communication with each other overa private network for enhanced data security. In another exampleembodiment, the servers 104 and the internal applications and thedatabases 105 are in communication with each other over the same network201.

As shown in FIG. 2, example components of the user device 102 include amicrophone, one or more other sensors, audio speakers, a memory device,one or more display devices, a communication device, and one or moreprocessors.

In an example embodiment, the user device's memory includes various“bots” that are part of the data enable application, which can alsoreside on the user device. In an example aspect, the one or more botsare considered chat bots or electronic agents. These bots includeprocessing that also resides on the 3^(rd) party cloud computing servers103. Examples of chat bot technologies that can be adapted to the systemdescribed herein include, but are not limited to, the trade names Siri,Google Assistant, and Cortana. In an example aspect, the bot used hereinhas various language dictionaries that are focused on various industries(e.g. including, but not limited to, sales and marketing terminology).In an example aspect, the bot used herein is configured to understandquestions and answers specific to various industries (e.g. sales andmarketing, etc.). In an example embodiment, the chat bots have access todifferent voice libraries associated with different people, and canspeak using a synthesized voice using a given one of the voicelibraries.

In an example aspect, the bot used herein learns the unique voice of theuser, which the bot consequently uses to learn behavior that may bespecific to the user. This anticipated behavior in turn is used by thedata enablement system to anticipate future questions and answersrelated to a given topic. This identified behavior is also used, forexample, to make action recommendations to help the user achieve aresult, and these action recommendations are based on the identifiedbehaviors (e.g. identified via machine learning) of successful users inthe same industry. In an example application, the questions and answersare for a given sales opportunity, and the recommendations and thebehaviors relates to achieving sales and marketing goals.

In an example aspect, the bot applies machine learning to identifyunique data features in the user voice. Machine learning can include,deep learning. Currently known and future known algorithms forextracting voice features are applicable to the principles describedherein. Non-limiting examples of voice data features include one or moreof: tone, frequency (e.g. also called timbre), loudness, rate at which aword or phrase is said (e.g. also called tempo), phonetic pronunciation,lexicon (e.g. choice of words), syntax (e.g. choice of sentencestructure), articulation (e.g. clarity of pronounciation), rhythm (e.g.patterns of long and short syllables), and melody (e.g. ups and downs invoice). As noted above, these data features can be used identifybehaviors and meanings of the user, and to predict the content, behaviorand meaning of the user in the future. It will be appreciated thatprediction operations in machine learning include computing data valuesthat represent certain predicted features (e.g. related to content,behavior, meaning, action, etc.) with corresponding likelihood values.

The user device may additional or alternatively receive video data orimage data, or both, from the user, and transmit this data via a bot tothe data enablement platform. The data enablement platform is thereforeconfigured to apply different types of machine learning to extract datafeatures from different types of received data. For example, the 3^(rd)party cloud computing servers use natural language processing (NLP)algorithms or deep neural networks, or both, to process voice and textdata. In another example, the 3^(rd) party cloud computing servers usemachine vision, or deep neural networks, or both, to process video andimage data.

Turning to FIG. 3, an example embodiment of an oral communication device(OCD) 301 is shown, which operates in combination with the user device102 to reduce the amount of computing resources (e.g. hardware andprocessing resources) that are consumed by the user device 102 toexecute the data enablement functions, as described herein. In somecases, the OCD 301 provides better or additional sensors than a userdevice 102. In some cases, the OCD 301 is equipped with better oradditional output devices compared to the user device 102. For example,the OCD includes one or more microphones, one or more cameras, one ormore audio speakers, and one or more multimedia projects which canproject light onto a surface. The OCD also includes processing devicesand memory that can process the sensed data (e.g. voice data, videodata, etc.) and process data that has been outputted by the dataenablement platform 303. As noted above, the data enablement platform303 includes, for example, the servers 103, 104, 105, and 107.

As shown in FIG. 3, the OCD 301 is in data communication with the userdevice via a wireless or wired data link. In an example embodiment, theuser device 102 and the OCD 301 are in data communication using aBluetooth protocol. The user device 102 is in data communication withthe network 201, which is in turn in communication with the dataenablement platform 303. In operation, when a user speaks or takesvideo, the OCD 301 records the audio data or visual data, or both. TheOCD 301, for example, also pre-processes the recorded data, for example,to extract data features. The pre-processing of the recorded data mayinclude, either in addition or in the alternative, data compression.This processed data or the original data, or both, are transmitted tothe user device 102, and the user device transmits this data to the dataenablement platform 303, via the network 201. The user device 102 mayalso transmit contextual data along with the data obtained or producedby the OCD 301. This contextual data can be generated by the dataenablement application running on the user device 102, or by the OCD301.

Outputs from the data enablement platform 303 are sent to the userdevice 102, which then may or may not transmit the outputs to the OCD301. For example, certain visual data can be displayed directly on thedisplay screen of the user device 102. In another example embodiment,the OCD receives the inputs from the user device and provides the userfeedback (e.g. plays audio data via the speakers, displays visual datavia built-in display screens or built-in media projectors, etc.).

In an example embodiment, the OCD 301 is in data connection with theuser device 102, and the OCD 301 itself has a direct connection to thenetwork 201 to communicate with the data enablement platform 303.

Similar functionality is applicable to the other instance of the OCD 301that is in data communication with the desktop computer 302. Inparticular, it is herein recognized that many existing computing devicesand user devices are not equipped with sensors of sufficient quality,nor with processing hardware equipped to efficiently and effectivelyextract the features from the sensed data. Therefore, the OCD 301supplements and augments the hardware and processing capabilities ofthese computing devices and user devices.

In an example embodiment, a different example of a silent OCD 304 isused to record the language inputs of the user. The silent OCD 304includes sensors that detects other user inputs, but which are not thevoice. Examples of sensors in the silent OCD 304 include one or more of:brain signal sensors, nerve signal sensors, and muscle signal sensors.These sensors detect silent gestures, thoughts, micro movements, etc.,which are translated to language (e.g. text data). In an exampleembodiment, these sensors include electrodes that touch parts of theface or head of the user. In other words, the user can provide languageinputs without having to speaking into a microphone. The silent OCD 304,for example, is a wearable device that is worn on the head of the user.The silent OCD 304 is also sometimes called a silent speech interface ora brain computer interface. The silent OCD 304, for example, allows auser to interact with their device in a private manner while in ameeting (see FIG. 4A) or in public.

Turning to FIG. 4A, the OCD 301 and the corresponding data enablementapplication is shown being used in a meeting notes mode with variouspeople, each having their own respective user devices. 401, 402, 403,404, 405, 304. The OCD can also be used to record data (e.g. audio data,visual data, etc.) and provide data to people that do not have their ownuser device. The OCD records the oral conversation of the meeting to,for example, take meeting notes. In another aspect, the OCD also linksto the user devices to give them information, for example, in real-timeabout the topics being discussed during the meeting. The OCD alsoreduces the computing resources (e.g. hardware and processing resources)on the individual user devices.

In an example embodiment, the user 406 wears a silent OCD 304 toprivately interact using with the OCD 301. For example, the user's brainsignals, nerve signals, muscle signals, or a combination thereof, arecaptured are synthesized into speech. In this way, the user 406 can attimes give private or silent notes, commands, queries, etc. to the OCD301, and at other times, provide public notes, commands, queries, etc.to the OCD 301 that are heard by the other users in the meeting.

In an example embodiment, the user devices 401, 402, 403, 404, 405, 304are in data communication with the OCD 301 via a wireless connection, ora wired connection. In an example embodiment, some of the user devices401, 402 do not have Internet access, but other user devices 403, 404,405 do have Internet access over separate data connections X, Y and Z.Therefore, the OCD 301 uses one or more of these data connections X, Yand Z to transmit and receive data from the data enablement platform303.

The OCD may use different communication routes based on the availablebandwidth, which may be dictated by the user devices.

For example, the OCD parses a set of data to be transmitted to the dataenablement platform into three separate data threads, and transmitsthese threads respectively to the user devices 403, 404 and 405. Inturn, these data threads are transmitted by the user devices over therespective data connections X, Y and Z to the data enablement platform303, which reconstitute the data from the separate threads into theoriginal set of data.

Alternatively, the OCD uses just one of the data connections (e.g. X)and therefore funnels the data through the user device 403.

In another example embodiment, the OCD designates the data connections Xand Y, corresponding to the user deices 403 and 404, for transmittingdata to the data enablement platform 303. The OCD designates the dataconnection Z, corresponding to the user device 405, for receiving datafrom the data enablement platform 303.

The data obtained by the OCD, either originating from a user device orthe data enablement platform, can be distributed amongst the userdevices that are in communication with the OCD. The OCD can also providecentral user feedback (e.g. audio data, visual data, etc.) to the usersin the immediate vicinity.

It will be appreciated that the OCD therefore acts as a local input andoutput device that is central to the group of users. In another exampleaspect, the OCD also acts as a local central processing device toprocess the sensed data, or processed the data from the data enablementplatform, or both. In another example aspect, OCD also acts as a localcentral communication hub.

In an example embodiment, the OCD, either in the alternative or inaddition, the OCD has its own network communication device and transmitsand receives data, via the network 201, with the data enablementplatform 303.

The OCD provides various functions in combination with the dataenablement platform 303. In an example operation, the OCD provides anaudio output that orally communicates the agenda of the meeting. In anexample operation, the OCD records the discussion items that are spokenduring the meeting, and automatically creates text containing meetingminutes. In an example operation, the OCD monitors the flow of thediscussion and the current time, and at appropriate times (e.g. afterdetecting one or more of: pauses, hard stops, end of sentences, etc.)the OCD interjects to provide audio feedback about moving on to the nextagenda item that is listed in the agenda. A pause, for example, is agiven time period of silence.

In an example operation, the OCD monitors topics and concepts beingdiscussed and, in real-time, distributes ancillary and related dataintelligence to the user devices. In an example operation, the OCDmonitors topics and concepts being discussed and, in real-time,determines if pertinent related news or facts are to be shared and, ifso, interjects the conversation by providing audio or visual outputs (orboth) that provides the pertinent related news or facts. In an exampleaspect, the OCD interjects and provides the audio or visual outputs (orbot) at appropriate times, such as after detecting one or more of: apause (e.g. a time period of silence), a hard stop, an end of asentence, etc.

In another example operation, the OCD monitors topics and concepts beingdiscussed and, in real-time, determines if a user provided incorrectinformation and, if so, interjects the conversation by providing audioor visual output that provides the correct information. For example, thedetermination of incorrectness is made by comparing the discussed topicsin real-time with trusted data sources (e.g. newspapers, internaldatabases, government websites, etc.).

In another example operation, the OCD provides different feedback todifferent user devices, to suit the interests and goals specific thedifferent users, during the meeting.

In another example operation, the OCD uses cameras and microphones torecord data to determine the emotion and sentiment of various users,which helps to inform decision making.

In another example operation, each of the users can use their userdevices in parallel to interact with the OCD or the data enablementplatform, or both, to conduct their own research or make private notes(or both) during the meeting.

In another example aspect, private notes of a given user can be madeusing their own device (e.g. a device like the silent OCD 304 and thedevice 401), and public notes can be made based on the discussionrecorded at threshold audible levels by the OCD 301. The private notesfor example, can also be recorded orally or by silent speech using thesilent OCD 304. For the given user, the data enablement platform, ortheir own user device, will compile and present a compilation of boththe given user's private notes and public notes that are organized basedon time of creation or recordation. For example:

@t1: public notes;

@t2: public notes+given user's private notes;

@t3: public notes

@t4: given user's private notes;

@t5: public notes+given user's private notes.

In another example embodiment, the OCD includes one or more mediaprojectors to project light images on surrounding surfaces.

It will be appreciated that while the housing body of the OCD is shownto be cylindrical, in other example embodiments, it has differentshapes.

Turning to FIG. 4B, users in Location A are interacting with one or moreOCDs, and a user in a separate location (i.e. Location B) is interactingwith another OCD. Together, these users, although at different locationscan interact with each through digital voice and imagery data. The dataenablement platform processes their data inputs, which can include voicedata, image data, physical gestures and physical movements. These datainputs are then used to by the data enablement platform to providefeedback to the users.

At Location A, two OCD units 301 are in data communication with eachother and project light image areas 411, 410, 409, 408. These projectedlight image areas are positioned in a continuous fashion to provide, ineffect, a single large projected light image area that can surround orarc around the users. This produces an augmented reality or virtualreality room. For example, one OCD unit projects light image areas 411and 410, and another OCD unit projects light image areas 409 and 408.

Also at Location A is a user 407 that is wearing another embodiment ofan OCD 301 a. This embodiment of the OCD 301 a includes a microphone,audio speakers, a processor, a communication device, and otherelectronic devices to track gestures and movement of the user. Forexample, these electronic devices include one or more of a gyroscope, anaccelerometer, and a magnetometer. In an example embodiment, the OCD 301a is trackable using triangulation computed from radio energy signalsfrom the two OCD units 301 positioned at different locations (but bothwithin Location A).

The users at Location A can talk and see the user at Location B.

Conversely, the user at Location B is wearing a virtual reality oraugmented reality headset, which is another embodiment of an OCD 301 b,and uses this to talk and see the users at Location A. The OCDembodiment 301 b projects or displays images near the user's eyes, oronto the user's eyes. The OCD embodiment 301 b also includes amicrophone, audio speaker, processor, and communication device, amongstother electronic components. Using the OCD embodiment 301 b, the user isable to see the same images being projected onto one or more of theimage areas 411, 410, 409, and 408.

Turning to FIG. 5, example components that are housed within the OCD 301are shown. The components include one or more central processors 502that exchange data with various other devices, such as sensors 501. Thesensors include, for example, one or more microphones, one or morecameras, a temperature sensor, a magnetometer, one or more inputbuttons, and other sensors.

In an example embodiment, there are multiple microphones that areoriented to face in different directions from each other. In this way,the relative direction or relative position of an audio source can bedetermined. In another example embodiment, there are multiplemicrophones that are tuned or set to record audio waves at differentfrequency ranges (e.g. a microphone for a first frequency range, amicrophone for a second frequency range, a microphone for a thirdfrequency range, etc.). In this way, more definition of audio data canbe recorded across a larger frequency range.

In an example embodiment, there are multiple cameras that are orientedto face in different directions. In this way, the OCD can obtain a 360degree visual field of view. In another example, one or more camerashave a first field of a view with a first resolution and one or morecameras have a second field of view with a second resolution, where thefirst field of view is larger than the second field of view and thefirst resolution is lower than the second resolution. In a furtherexample aspect, the one or more cameras with the second field of viewand the second resolution can be mechanically oriented (e.g. pitched,yawed, etc.) while the one or more cameras with the first field of viewand the first resolution are fixed. In this way, video and images can besimultaneously taken from a larger perspective (e.g. the surroundingarea, people's bodies and their body gestures), and higher resolutionvideo and images can be simultaneously taken for certain areas (e.g.people faces and their facial expressions).

The OCD also includes one or more memory devices 503, lights 505, one ormore audio speakers 506, one or more communication devices 504, one ormore built-in-display screens 507, and one or more media projectors 508.The OCD also includes one or more graphics processing units (GPUs) 509.GPUs or other types of multi-threaded processors are configured forexecuting AI computations, such as neural network computations. The GPUsare also used, for example, to process graphics that are outputted bythe multimedia projector(s) or the display screen(s) 507, or both.

In an example embodiment, the communication devices include one or moredevice-to-device communication transceivers, which can be used tocommunicate with one or more user devices. For example, the OCD includesa Bluetooth transceiver. In another example aspect, the communicationdevices include one or more network communication devices that areconfigured to communicate with the network 201, such as a network cardor WiFi transceiver, or both.

In an example embodiment, there are multiple audio speakers 506positioned on the OCD to face in different directions. In an exampleembodiment, there are multiple audio speakers that are configured toplay sound at different frequency ranges.

In an example embodiment, the built-in display screen forms a curvedsurface around the OCD housing body. In an example embodiment, there aremultiple media projectors that project light in different directions.

In an example embodiment, the OCD is able to locally pre-process voicedata, video data, image data, and other data using on-board hardware andmachine learning algorithms. This reduces the amount of data beingtransmitted to the data enablement platform 303, which reduced bandwidthconsumption. This also reduces the amount of processing required by thedata enablement platform.

FIGS. 6 and 7 show example computing architectures of the dataenablement platform, which are in alternative to the above-discussedarchitectures. In another example, these computing architectures shownin FIGS. 6 and 7 are incorporated into the above-discussedarchitectures.

Turning to FIG. 6, an example computing architecture 601 is provided forcollecting data and performing machine learning on the same. Thisarchitecture 601, for example, is utilized in the AI platform 107.

The architecture 601 includes one or more data collector modules 602that obtain data from various sources, such as IoT devices, enterprisesoftware, user generated websites and data networks, and public websitesand data networks. Non-limiting examples of IoT devices include sensorsused to determine the status of products (e.g. quantity of product,current state of product, location of product, etc.). IoT devices canalso be used to determine the status of users (e.g. wearable devices).IoT devices can also be used to determine the state of equipment,environment, and facilities that facilitate the ability for a company toprovide product or service, or both. Enterprise software can include CRMsoftware and sales software. User generated data includes social datanetworks, messaging applications, blogs, and online forums. Publicwebsites and data networks include government websites and databases,banking organization websites and databases, economic and financialaffairs websites and databases. It can be appreciated that other digitaldata sources may be collected by the data collector modules.

The collected data is transmitted via a message bus 603 to a streaminganalytics engine 604, which applies various data transforms and machinelearning algorithms. For example, the streaming analytics engine 604 hasmodules to transform the incoming data, apply language detection, addcustom tags to the incoming data, detect trends, and extract objects andmeaning from images and video. It will be appreciated that other modulesmay be incorporated into the engine 604. In an example implementation,the engine 604 is structured using one or more of the following big datacomputing approaches: NiFi, Spark and TensorFlow.

NiFi automates and manages the flow of data between systems. Moreparticularly, it is a real-time integrated data logistics platform thatmanages the flow of data from any source to any location. NiFi is datasource agnostic and supports different and distributes sources ofdifferent formats, schemas, protocols, speeds and sizes. In an exampleimplementation, NiFi operates within a Java Virtual Machine architectureand includes a flow controller, NiFi extensions, a content repository, aflowfile repository, and a provenance repository.

Spark, also called Apache Spark, is a cluster computing framework forbig data. One of the features of Spark is Spark Streaming, whichperforms streaming analytics. It ingests data in mini batches andperforms resilient distributed dataset (RDD) transformations on thesemini batches of data.

TensorFlow is software library for machine intelligence developed byGoogle. It uses neural networks which operate on multiple centralprocessing units (CPUs), GPUs and tensor processing units (TPUs).

Offline analytics and machine learning modules 610 are also provided toingest larger volumes of data that have been gathered over a longerperiod of time (e.g. from the data lake 607). These modules 610 includeone or more of a behavior module, an inference module, a sessionizationmodule, a modeling module, a data mining module, and a deep learningmodule. These modules can also, for example, be implemented by NiFi,Spark or TensorFlow, or combinations thereof. Unlike these the modulesin the streaming analytics engine 604, the analysis done by the modules610 is not streaming. The results are stored in memory (e.g. cacheservices 611), which then transmitted to the streaming analytics engine604.

The resulting analytics, understanding data and prediction data, whichare outputted by the streaming analytics engine 604, are transmitted toingestors 606, via the message bus 605. The outputted data from theoffline analytics and machine learning modules 610 can also betransmitted to the ingestors 606.

The ingestors 606 organize and store the data into the data lake 607,which comprise massive database frameworks. Non-limiting examples ofthese database frameworks include Hadoop, HBase, Kudu, Giraph, MongoDB,Parquet and MySQL. The data outputted from the ingestors 606 may also beinputted into a search platform 608. A non-limiting example of thesearch platform 608 is the SoIr search platform built on Apache Lucene.The SoIr search platform, for example, provides distributed indexing,load balanced querying, and automated failover and recovery.

Data from the data lake and the search engine are accessible by APIservices 609.

Turning to FIG. 7, another architecture 701 is shown, which used afterthe data has been stored in the data lake 607 and indexed into thesearch platform 608.

A core services module 702 obtains data from the search platform 608 andthe data lake 607 and applies data science and machine learningservices, distributed processing services, data persistence services tothe obtained data. For example, the data science and machine learningservices are implemented using one or more of the followingtechnologies: NiFi, Spark, TensorFlow, Cloud Vision, Caffe, Kaldi, andVisage. It will be appreciated that other currently known and futureknown data science or machine learning platforms can be used to executealgorithms to process the data. Non-limiting examples of distributedprocessing services include NiFi and Spark.

The API services module 609 includes various APIs that interact with thecore services module 702 and the applications 704. The API servicesmodule 609, for example, exchanges data with the applications in one ormore of the following protocols: HTTP, Web Socket, Notification, andJSON. It will be appreciated that other currently known or future knowndata protocols can be used.

The module 609 includes an API gateway, which accesses various APIservices. Non-limiting examples of API service modules include anoptimization services module, a search services module, an algorithmservices module, a profile services module, an asynchronous servicesmodule, a notification services module, and a tracking services module.

In an example embodiment, the modules 609 and 702 are part of the AIplatform 107, and the applications 704 reside on one or more of the datascience servers 104, the internal applications and databases 105, andthe user device 102. Non-limiting examples of the applications includeenterprise business applications, AI applications, system managementapplications, and smart device applications.

Turning to FIG. 8, an example embodiment of an AI XD platform 109 isshown, comprising various types of Intelligent Devices represented bydifferent sized boxes, according to an embodiment described herein. TheAI XD platform 109 includes, for example, a plurality of intelligentdevices, intelligent device message buses, and networks. The variousIntelligent Devices can be dispersed throughout the platform. Similar toa human brain with neurons and synapses, neurons can be considered akinto Intelligent Edge Nodes and synapses can be considered akin toIntelligent Networks. Hence, Intelligent Edge Nodes are distributed andconsequently support the notion of distributed decision making—animportant step and embodiment in performing XD decision scienceresulting in recommendations and actions. However, unlike the synapsesof a human brain, the Intelligent Networks in the platform 109 asdisclosed herein can have embedded “intelligence”, wherein intelligencecan refer to the ability to perform data or decision science, executerelevant algorithms, and communicate with other devices and networks.

Intelligent Edge Nodes are a type of an Intelligent Device, and cancomprise various types of computing devices or components such asprocessors, memory devices, storage devices, sensors, or other deviceshaving at least one of these as a component. Intelligent Edge Nodes canhave any combination of these as components. Each of the aforementionedcomponents within a computing device may or may not have data ordecision science embedded in the hardware, such as microcode data ordecision science running in a GPU, data or decision science runningwithin the operating system and applications, and data or decisionscience running as software complimenting the hardware and softwarecomputing device.

As shown in FIG. 8, the AI XD platform 109 can comprise variousIntelligent Devices including, but not limited to, for example, an AlgoFlashable Microcamera with WiFi Circuit, an Algo Flashable Resistor andTransistor with WiFi Circuit, an Algo Flashable ASIC with WiFi Circuit,an Algo Flashable Stepper Motor and Controller WiFi Circuit, AlgoFlashable Circuits with WiFi Sensors, and an ML Algo Creation andTransceiver System. Intelligent Devices listed above may be “AlgoFlashable” in a sense that the algorithms (e.g., data or decisionscience related algorithms) can be installed, removed, embedded,updated, loaded into each device. Other examples of Intelligent Devicesinclude user devices and OCDs.

Each Intelligent Device in the platform 109 can perform general orspecific types of data or decision science, as well as perform varyinglevels (e.g., complexity level) of computing capability (data ordecision science compute, store, etc.). For example, Algo FlashableSensors with WiFi circuit may perform more complex data sciencealgorithms compared to those of Algo Flashable Resistor and Transistorwith WiFi circuit, or vice versa. Each Intelligent Device can haveintelligent components including, but not limited to, intelligentprocessors, RAM, disk drives, resistors, capacitors, relays, diodes, andother intelligent components. Intelligent Networks (represented asbi-directional arrows in FIG. 8) can comprise one or more combinationsof both wired and wireless networks, wherein an Intelligent Networkincludes intelligence network devices, which are equipped with orconfigured to apply data or decision science capabilities.

Each Intelligent Device can be configured to automatically andautonomously query other Intelligent Devices in order to better analyzeinformation and/or apply recommendations and actions based upon, or inconcert with, one or more other Intelligent Devices and/or third partysystems. This exemplifies applying perfect or near perfect information,by using as much data and data or decision science prior to taking anaction given all information that is available at that specific moment.

Each Intelligent Device can also be configured to predict and determinewhich network or networks, wired or wireless, are optimal forcommunicating information based upon local and global parametersincluding but not limited to business rules, technical metrics, networktraffic conditions, proposed network volume and content, andpriority/severity levels, to name a few. An Intelligent Device canoptionally select a multitude of different network methods to send andreceive information, either in serial or in parallel. An IntelligentDevice can optionally determine that latency in certain networks are toolong or that a certain network has been compromised, for example, byproviding or implementing security protocols, and can reroute contentusing different encryption methods and/or reroute to different networks.An Intelligent Device can optionally define a path via for example nodesand networks for its content. An Intelligent Device can optionally usean Intelligent Device Message Bus to communicate certain types ofmessages (e.g. business alerts, system failures) to other IntelligentDevices. One or more Intelligent Device Message Buses can connectmultiple devices and/or networks.

Each Intelligent Device can optionally have an ability to reduce noiseand in particular, to reduce extreme data, especially at the local levelor through the entire platform 109. This may provide the platform 109the ability to identify eminent trends and to make preemptive businessand technical recommendations and actions faster, especially since lessduplicative data or extreme data allows for faster identification andrecommendations.

Each Intelligent Device can include data or decision science softwareincluding but not limited to operating systems, applications, anddatabases, which directly support the data or decision science drivenIntelligent Device actions. Linux, Android, MySQL, Hive, and Titan orother software could reside on System-on-Chip devices so that the localdata or decision science can query local, on device, related data tomake faster recommendations and actions.

Each Intelligent Device can optionally have an Intelligent Policy andRules System. The Intelligent Policy and Rules System provides governingpolicies, guidelines, business rules, nominal operating states, anomalystates, responses, key performance indicator (KPI_metrics, and otherpolicies and rules so that the distributed IDC devices can make localand informed autonomous actions following the perfect informationguiding premise as mentioned above. A number (e.g., NIPRS) ofIntelligent Policy and Rules Systems can exist, and the aforementionedsystems can have either identical, or differing policies and rulesamongst themselves or alternatively can have varying degrees or subsetsof policies and rules. This latter alternative is important when thereare localized business and technical conditions that may not beappropriate for other domains or geographic regions.

Turning to FIG. 9, example computer executable instructions are providedfor processing data using the data enablement platform. At block 909, auser device or an OCD, or both, receives input to select a function or amode of an application (e.g. the data enablement application) thatresides on the user device. At block 902, the user device or the OCD, orboth, obtains voice data from a user. At block 903, the user device orthe OCD, or both, transmits the same data to the 3^(rd) party cloudcomputing servers. The user device also transmits, for example,contextual data. At block 904, the 3^(rd) party cloud computing serversprocesses the voice data to obtain data features.

Non-limiting examples of extracted data features include text,sentiment, action tags (e.g. commands, requests, questions, urgency,etc.), voice features, etc. Non-limiting examples of contextual featuresinclude the user information, device information, location, function ormode of the data enablement application, and a date and time tag.

In an alternative example embodiment, the voice data is processedlocally on the user device to generate text (e.g. using speech-to-textprocessing) and the text is sent to the servers for further processing.

The extracted data features and the contextual features are transmittedto the data science servers (block 905). The original data (e.g. rawaudio data) may also be transmitted to the data science servers. Atblock 906, the data science servers process this received data.

At block 907, the data science servers interact with the AI platform, orthe internal applications and internal databases, or both, to generateone or more outputs.

The data science servers then send the one or more outputs to the 3^(rd)party cloud computing servers (block 908). In one example embodiment,the 3^(rd) party cloud computing servers post-processes the outputs toprovide or compile text, image, video or audio data, or combinationsthereof (block 909). At block 910, the 3^(rd) party cloud computingservers transmit the post-processed outputs to the relevant userdevice(s) or OCD(s). At block 911, the user device(s) or the OCD(s), orboth, output the post-processed outputs, for example, via an audiodevice or a display device, or both.

In an alternative embodiment, stemming from block 908, the 3^(rd) partycloud computing server transmits the outputs to the one or more relevantdevices (e.g. user devices or OCDs) at block 912. The post-processing isthen executed locally on the one or more relevant devices (block 913).These post-processed outputs are then outputted via audio devices orvisual devices, or both on the one or more user devices or OCDs (block911).

Turning back to block 907, in an example aspect, the data scienceservers pull data from the internal applications and internal databases,or the internal applications and internal database are updated based onthe results produced by the data science servers, or both (block 914).

In another example aspect, the data science servers transmit data andcommands to the AI platform, to apply AI processes on the transmitteddata. In return, the AI platform transmits external and localinformation and data intelligence to the data science servers. Theseoperations are shown in block 915.

It can be appreciated that any two or more of the operations in blocks907, 914, and 915 can affect each other. In an example embodiment, theoutputs of block 914 are used in the operations of block 915. In anotherexample embodiment, the outputs of block 915 are used in the operationsof block 914.

It is herein recognized that the devices, systems and the methodsdescribed herein enable the provision of actionable data that can beused in various industries. One example industry, among other applicableindustries, is sales and marketing.

Sales people, sales management, and senior executives continue to wastea lot of time understanding, qualifying, evaluating, and predictingsales opportunities. The devices in combination with the data enablementplatform provides the aforementioned people with “Perfect Information”,a concept from economists.

A software example applying perfect information is the Bloombergterminal. This software platform integrates and display all globalexchanges (stock markets, currency, natural resources, etc.), globalnews that impacts industries and companies, and the ability to buy andsell on these exchanges to provide traders with the most up to dataglobal “perfect information” to make trades.

By way of analogy, the data enable platform described herein, incombination with the user device or the OCD, or both, provide perfectinformation to help sales organizations integrate and display allinformation related to sales and sales opportunities. A user, forexample, talks with a bot on the user device or the OCD.

The bot engages with the sales people, sales managers, and executives byautonomously capturing, analyzing, recommending, and taking actionsrelated to leads and opportunities. Examples include: creating new leadsand opportunities spoken by the salesperson to the bot; the botconversing with the salesperson about a new executive champion at theopportunity company; the bot conversing with the salesperson about a newfeature released in a competitive product that could increaseopportunity closure risk; the bot conversing with the salesperson toprovide specific information about the opportunity (e.g. executiveopportunity sponsorship, confirmed budget, etc.) to move the opportunityfrom one sales process step to the next; and, the bot conversing withthe salesperson requesting permission to automatically move theopportunity into the CRM system because all of the information is nowvalidated and ready for the CRM systems and applications. It will beappreciated that other example actions and interactions can be performedusing the devices, systems and the methods described herein.

In an example aspect, there are N number of specialized bots residingand operating within the data enablement platform, the user device, orthe OCD, or a combination thereof.

These sales and marking bots make distributed and autonomous decisionscience based recommendations and actions that increasingly becomesmarter and faster over time as the bot and salesperson interact moreand more. In particular, a bot assigned to a specific salesperson beginsto learn the salesperson's patterns and behaviors and subsequently makesrecommendations and provides opportunity updates based on salesperson'sbehavior, likes, and dislikes. The bot also recommends actions and bestpractices from other top salespeople in the sales organization that helpthe given salesperson increase their own sales process and ultimatelyhelp close deals faster.

In preferred embodiments, the bot is a chat bot that has languagecapabilities to interact with the user via text language or spokenlanguage or both. However, in other example embodiment, the bot does notnecessarily chat with a user, but still affects the display of databeing presented to the user.

Turning to FIG. 10, an example embodiment is provided of softwaremodules that reside on a given user device 1001, data science servers1004, and internal applications and databases 1006, which are suited forenabling sales and marketing actions.

For example, a data enablement application 1002 resides on the userdevice and the application includes: a to-do list module, a meetingnotes module, an opportunity news module, an opportunities module, anintroductions module, and a configuration module. The user device alsoincludes user interface (UI) modules 1003, which can be part of the dataenablement application 1002, or may interact with the data enablementapplication 1002. The UI modules include one or more chat bots, one ormore voice libraries/modules associated to be utilized by the one ormore chatbots, one or more GUIs, one or more messaging application, oneor more tactile feedback modules, or combinations thereof.

The data science servers 1004 include a data science algorithms library,a to-do list module, a meeting notes module, an opportunity news module,an opportunities module, an introductions module, a configurationmodule, and a policy and rules engine. For example, the policy and rulesengine includes policies and rules that are specific to the company ororganization using the data enablement platform.

In an example aspect, the policy and rules engine is a data sciencedriven system that autonomously prompts the salesperson to progressivelyadd opportunity related data to an opportunity. The opportunity data maycome in the form of autonomously collected data from the salesenablement platform, data directly inputted (oral or typed in) by thesalesperson, or data captured during meetings (audio to text to NLPprocessing), or any combination of the aforementioned.

In another example aspect, this policy and rules engine helps ensure thesalesperson is complying with the sales organization sales process sothat data is correct, accurate, and timely submitted into theintelligent sales enablement system prior to moving the complete andtimely opportunity information into a traditional CRM application.Autonomously performing these sales process steps along the wayincreases the data accuracy and timeliness as opposed to last minute andhaphazardly trying to recall and enter opportunity data into a CRMsystem.

In other example embodiments, this policy and rules engine can reside onthe user's smartphone, or in public or private clouds, or at theemployee's data center, or any combination of the aforementioned.

Regarding the data science algorithms library, it will be appreciatedthat data science herein refers to math and science applied to data inthe form including but not limited to algorithms, machine learning,artificial science, neutral networks, etc. The results from data scienceinclude, but are not limited to, business and technical trends,recommendations, actions, trends, etc.

In an example aspect, Surface, Trend, Recommend, Infer, Predict andAction (STRIPA) algorithms are included in the data science algorithmslibrary. This family of STRIPA algorithms worth together and are used toclassify specific types of data science to related classes.

Non-limiting examples of other data science algorithms that are in thedata science library include: Word2vec Representation Learning;Sentiment (e.g. multi-modal, aspect, contextual, etc.); Negation cue,scope detection; Topic classification; TF-IDF Feature Vector; EntityExtraction; Document summary; Pagerank; Modularity; Induced subgraph;Bi-graph propagation; Label propagation for inference; Breadth FirstSearch; Eigen-centrality, in/out-degree; Monte Carlo Markov Chain (MCMC)simulation on GPU; Deep Learning with region based convolutional neuralnetworks (R-CNN); Torch, Caffe, Torch on GPU; Logo detection; ImageNet,GoogleNet object detection; SIFT, SegNet Regions of interest; SequenceLearning for combined NLP & Image; K-means, Hierarchical Clustering;Decision Trees; Linear, Logistic regression; Affinity Association rules;Naive Bayes; Support Vector Machine (SVM); Trend time series; Burstanomaly detection; KNN classifier; Language Detection; Surfacecontextual Sentiment, Trend, Recommendation; Emerging Trends; WhatsUnique Finder; Real-time event Trends; Trend Insights; Related QuerySuggestions; Entity Relationship Graph of Users, products, brands,companies; Entity Inference: Geo, Age, Gender, Demog, etc.; Topicclassification; Aspect based NLP (Word2Vec, NLP query, etc.); Analyticsand reporting; Video & audio recognition; Intent prediction; Optimalpath to result; Attribution based optimization; Search and finding; andNetwork based optimization.

In other example embodiments, the aforementioned data science can resideon the user's smartphone, or in public or private clouds, or at theemployee's data center, or any combination of the aforementioned.

Continuing with FIG. 10, UI modules 1005 also reside on the data scienceservers 1004.

The internal applications and database 1006 also include varioussoftware and database that are used to assist in sales and marketingactions. These include CRM software, email software, calendar software,contact list software, project management software, accounting software,and inventory software.

Turning to FIG. 11, an example data flow diagram shows the flow of databetween different modules. Data can flow between the to-do list module1101, the opportunities module 1102, the introductions module 1103, themeeting notes module 1104, and the opportunity news module 1106 invarious combinations other than what is shown in FIG. 11.

However, FIG. 11 does provide an example embodiment. The meeting notesmodule records meeting notes (e.g. via audio input) and generatesmeeting data. This meeting data is transmitted to the introductionsmodule, the opportunities module and the to-do list module. In anexample embodiment, the meeting data includes data obtained in a meetingsetting (see for example FIG. 4A).

The meeting data is used by the introductions module to determinerelevant contact information and relationship data, which aretransmitted to the opportunities module and the to-do list module.

The opportunities module uses the relationship data and the meeting datato determine important opportunities. The opportunities module alsoobtains data from the opportunities news module to determine newopportunities and important opportunities.

The to-do list module obtains the opportunity potential data from theopportunities module, the contact information from the introductionsmodule, and the meeting data from the meeting notes module to generateaction items and prioritize the action items for the salesperson. Forexample, an action item is created to have lunch with a first certaincontact to discuss a potential opportunity with a second certaincontact, and this action item is prioritized as urgent, given the salesopportunity. The to-do list module interacts with the calendar module1108 to automatically schedule actions.

The opportunity news module is able to interact with the other modulesto obtain related external data or related internal data, or both.

For example, a user is in a current given mode (e.g. a meeting notesmode). The user's input generates a response from the data enablementapplication that includes text data, an indication of a voice library,and the current mode. The user device receives this response andpropagates the text to other modules associated with other modes thatare not currently active (e.g. the to-do list module, the opportunitiesmodule, the introduction module). In this way, the other modules receiveupdated data that are relevant to their functions.

In an example embodiment, when a user is view a given meeting, the usercan view the meeting notes, the related action items generated by theto-do list module, the related opportunities generated by theopportunities module, the related warm introductions generated by theintroductions module, and the related news obtained from the opportunitynews module.

In example aspects, these modules use machine learning to learn andmodel opportunities that are right for the organization; learn and modelthe right tasks that a salesperson should perform from the day anopportunity arises through ongoing upsell/cross sell customeropportunities; learn and model best practices performed by othersalespeople that may apply to a saleperson(s); learn and model whattasks a salesperson should perform daily, weekly, etc. for eachopportunity; learn and model what tasks and reports that a sale managerneeds to optimize his sales teams performance; and learn and model thetasks and behaviors that the CEO and other executives need in order topredict, with confidence, revenue. These are just a few of thelearn-and-model practices that the data enablement system performs.Actions in response to these machine learning models include, but arenot limited to, taking autonomous actions such as sending prospectsrelevant emails, sending reminder prompts requesting data from asalesperson, reminding the salesperson about upcoming meetings, etc.

FIGS. 12-19 include screenshots of example GUIs shown for applying thedata enablement system to sales and marketing.

In FIG. 12, a home landing page 1201 for the data enablementapplication. It includes GUI controls for a my to-do list 1202, myopportunities 1203, my meeting notes 1204, my warm introductions 1205,my opportunity news 1206, and configuration settings 1207.

By selecting the My To Do List control 1202, a conversational andintelligent AI bot is launched that automatically recommends andprioritizes tasks for the salesperson based on notes, meetings withcustomers, calendar events, etc. each day and updated real time.

By selecting the My Opportunities control 1203, a conversational andintelligent AI bot is launched that automatically creates, updates, anddeletes opportunities and real time updates opportunity informationusing a baseball card-like metaphor.

By selecting the My Meeting Notes control 1204, a conversational andintelligent AI bot is launched that can record customer meetings, salesmanager meetings, salesperson's reminder notes and apply NLP and STRIPA.The results from the NLP and STRIPA subsequently updates the My To Dolist bot, My Opportunity bot, etc.

By selecting the My Warm Intros control 1205, a conversational andintelligent AI bot is launched that automatically searches my peers,friends, acquaintances, former employees and bosses, etc. who arerelated to an opportunity and that could help provide insights andprovide access to decision makers, executive sponsors, etc. at a lead oropportunity

The conversational bot orally provides new information to thesalesperson if the salesperson is currently working that opportunity toprovide the faster information in order to help the salesperson and alsoprovides this update in the My Opportunities area

By selecting a My Opportunity News control 1206, a conversational andintelligent AI bot is launched that automatically searches pressreleases, news, social sites, blogs, forums, etc. that related to anopportunity and that helps provide insights and recommendations to thesalesperson so that the saleperson is armed with all of the most up todate information to understand and close the opportunity. Theconversational bot orally provides new information to the salesperson ifthe salesperson is currently working that opportunity to provide thefaster information in order to help the salesperson and also providesthis update in the My Opportunities area

By selecting the Configuration settings control 1207, the setup,configuration, and preferences section of the application are launched.

A search field 1208 in this GUI does a global level search across allthe modules based on the user's inputted keyword (e.g. inputted viavoice or by typing).

The depicted control elements are for example. Other control elementswith different data science, bots, features, and functionality may beadded and mixed with other control elements.

Turning to FIG. 13, an example embodiment of a My To Do List page GUI1301 is shown. Under this mode, a conversational question and answer bottalks with an account representative to orally create new opportunitytasks, answer outstanding tasks and mark complete tasks. The bot alsoallows the account representative to orally enter opportunity data. Inanother example aspect, the bot is uses AI to provide a recommended tasklist that is ordered based on importance. The bot dynamically updatesthe task list and the prioritization based on the status of new tasksentered, new data becoming available, and the completion of tasks. Thebot also reminds the account rep to orally enter company requiredopportunity data. The bot then transmits this opportunity data to theCRM software, for example, when the account representative is ready forCRM review. Using this system, an account representative can morenaturally provide relevant information, and the bot will extract,structure and organize the information for storage in the CRM software.The bot is also able use AI to perform sales forecasts.

A search field 1302 in this GUI takes the user's input (e.g. voice inputor typed input) to conduct a search of the recommended to-do-list.

Turning to FIG. 14, an example embodiment of a My Opportunities page GUI1401 is shown. Under this mode, a conversational question and answer bottalks with a user to search for opportunities and to sort theopportunities. For example, the user can orally speak to the bot tosearch for a specific opportunity related to a certain company,business, industry, or person. The user can also orally command the botto sort the opportunities list, such as by dollar amount, past closurerate, alphabetically, predicted chance of closing the deal, or acombination thereof. Other features may be used to sort theopportunities list. The user may also orally control the both regardingwhether or not a given opportunity is to be loaded into the CRM software(e.g. SalesForce.com platform). The bot also generates or identifiesrelated action items specific to a given opportunity, and orallypresents them to the user.

The GUI 1401 also conveys this information visually. For example, asearch field 1402 is shown in the GUI, as well as various controls 1403to sort the list of opportunities. For example, the search field 1402 isused to implement a search within the opportunities module. A statusflag 1404 is also displayed to indicate whether or not the opportunityhas been loaded into the CRM software. The level of risk of a givenopportunity is also visually displayed by color coding each entry in thelist 1405.

Turning to FIG. 15, a GUI 1501 showing My Meeting Notes includes dailynews items 1502 that are related to certain opportunities. Below the newitems, there are action notes (e.g. voice data, other data) andreminders 1503 which are prioritized using machine learning algorithms.In an example embodiment, a chat bot provides audio news and uses audiooutput to relay the action notes. There is also a summary of theopportunity status 1505. A search field 1504 is used to launch a searchwithin the meeting notes module.

Turning to FIG. 16, a GUI 1601 shows an AI driven list of people 1603(e.g. from LinkedIn, Outlook, Gmail, social network database of peers,etc.) who can potentially help the account representative to accessopportunities and to gain further opportunity insights. The GUI alsoincludes voice notes 1604 regarding to-do notes or action items that arespecific to a given person on the list 1603. A search field 1602 mayalso be included to facilitate a user to search within the warm introlist module.

Turning to FIG. 17, a GUI shows opportunity news controls for variouscompanies (e.g. sales opportunities). When a user verbally selects acontrol or taps a control for a given company, a screen is displayedthat shows news items for the given company.

For example, FIGS. 18 and 19 show GUIs containing the news items for thegiven company. The news items are sorted, for example, according thenews that provides the greatest sales opportunity for the givensalesperson, as determined using machine learning.

Below are example questions and statement posed by a user, and oralfeedback provided by the chatbot. It will be appreciated that the bot orchatbot is conversational and adapts to the style of the user to whichit is speaking.

Example 1

User: Hey Bot, provide me with news about opportunity X.Bot: Hey User, here is the latest new on customer opportunity X.The Bot reads out the latest 3 to 5 latest new summaries pulled fromvarious data sources.

Example 2

User: Hey Bot, how much commission do I make on a customer x deal?Bot: Hey User, if sell 100,000 software license seats, you will earn350,000 dollars, the largest seat deployment in the company.

Example 3

User: Hey Bot, tell me the pricing for this product or service?Bot: Hey User, the retail price is 70 dollars per seat for less than50,000 seat subscription, 50 dollars per seat for 50,000 to 150,000seats, and 35 dollars per seat for over 150,000 seats.

Example 4

User: Hey Bot, tell me the best system engineer or product manager thatknow this product.Bot: Hey User, Jacob Smith is the best subject matter expert based onhis recent customer feedback and the product managers feedback.In an example aspect, the data enablement platform applies NLP sentimentprocessing on internal company reviews, intranet website comments, andother data sources regarding user performance.

Example 5

User: Hey Bot, tell me 3 to 5 things I know about the key decision makerat customer X or prospect X?Bot: Hey User, John Smith is the CFO and LinkedIn suggest that he is thedecision maker on this initiative. Here is a link to John Smith's bio atLinkedIn.

Example 6

User: Hey Bot, tell me which customers over 50% closure I should talkwith next?Bot: Hey User, you should focus on the Google opportunity, the Lenovoopportunity, and the CocaCola opportunity based on your previous notes.

Example 7

User: Hey Bot, summarize and tell me my notes and actions form my lastmeeting at Lenovo.Bot: Hey User, there are 3 Lenovo actions you need to complete. Firstcall Alice Anders and see if you can learn more about the dollar budgetfor this initiative. Second, call Bob Bingham and see if this initiativeis budgeted for 2017. And third, you need to talk to Jacob Smith to seeif the API will support the number of requests.

Turning to FIG. 20, an example computation is shown for applying naturallanguage processing (NLP). At block 2001, the user device or the OCDreceives input to monitor a given company. At block 2002, at regularintervals (e.g. daily), the data enablement platform executes externalsearches for the latest news regarding a given company, the industry ofthe given company, the competitors of the given company, the financialsof the given company, etc. At block 2003, the external search resultsare stored in memory. At block 2004, the data enablement platformapplies NLP automatic summarization of the search results and outputsthe summarization to the user device (e.g. via audio feedback) (block2005). The process then repeats at regular intervals, as per block 2002.

Turning to FIG. 21, another example computation is provided. At block2101, the user device or the OCD receives input to monitor a givencompany. At block 2102, at regular intervals (e.g. daily), the dataenablement platform executes external searches for the latest newsregarding a given company, the industry of the given company, thecompetitors of the given company, the financials of the given company,etc. At block 2103, the external search results are stored in memory. Atblock 2104, the data enablement platform executed internal searches forthe given company. At block 2105, these internal search results arestored. At block 2106, the data enablement platform compares theexternal search results with the internal search results to determine ifthey affect each other. For example, the data enablement platformdetermines if there are differences in the data or similarities in thedata, or both. At block 2107, the data enablement platform applies NLPautomatic summarization of the affected external search results, or theaffected internal search results, or both. The summarization isoutputted to the user device for visual display or audio feedback (block2108). In this way, a user is informed of relevant news and why the newsis relevant (e.g. affected internal data, opportunities, etc.).

In an example embodiment, the above methods in FIG. 20 or 21 are used toprovide a bot, or chatbot, that provides a fast and easy way to consumenews summaries (e.g. press releases, product and competitor reviews,financials, LinkedIn, Facebook fan page, etc.) for each specific salesopportunity and creates an opportunity score card using machine learningand other data science algorithms. This saves time and increasesaccuracy on new leads and renewal efforts.

Turning to FIG. 22, example executable instructions are provided forusing K-nearest neighbor computations to identify contacts that areconsidered close to a given company or given user, in relation to asales opportunity.

Block 2201: Receive input from a user device identifying a givencompany/person.

Block 2202: At regular intervals (e.g. daily) execute external searchesfor contacts, friends, followers, friends of friends, etc. of the givencompany/person that are coming to the user.

Block 2203: Store external search results.

Block 2204: Execute internal search for contacts, friends, followers,friends of friends, etc. of the given company/person that are common tothe user.

Block 2205: Store internal search results.

Block 2206: Combine external search and internal search results todevelop a relationship-feature dataset involving the givencompany/person.

Block 2207: Apply K-nearest neighbor to the relationship-feature datasetand prioritize the list of names by the “strongest relationship”neighbor to the given.

Block 2208: Output the list of names to the user device, and if thereare changes in subsequent searches, inform the user device of thechanges.

In an example implementation of the above computing operations, the WarmIntro Bots is provided, which dynamically searches and presents a listof employees, friends, and acquaintances that the account representativeneeds to leverage in order to get a warm opportunity introduction andobtain respective opportunity insights using search and graph datascience. Warm leads have higher close rates.

In another example aspect, the data enablement platform uses keywords,sentences, full conversations and hashtags to characterize theopportunity. The data enablement platform subsequently performs a searchfor existing internal opportunities so that one given salesperson doesnot spend time on the same opportunity being pursued by a peersalesperson.

Turning to FIG. 23, another example embodiment of executableinstructions are provided for using regression analysis to determine anopportunity baseline in dollar value.

Block 2301: Receive input from a user device that identifies a givencompany.

Block 2302: Query internal CRM system about information regardingopportunities, closed deals, lost deals etc.

Block 2303: Segment opportunities, closed deals, etc. by the industrysegment(s) that is/are relevant to the given company.

Block 2304: Perform regression analysis on the segmented industry CRMdata creating an opportunity $ baseline for an opportunity at each salesstage.

Block 2305: Compare the monetary opportunity in relation to the givencompany (proposed by the salesperson), at its current sales stage, tothe industry segment $ baseline opportunity.

Block 2306: Present the variance between the given company $ opportunityand the industry segment $ baseline.

Block 2307: Compute and output to the user device a proposed discountmultiplier against the given company $ opportunity (proposed by thesalesperson) based on machine learning related deals in the sameindustry.

Block 2308: Compute and output a proposal of the revised sales pipelinerevenue related to this opportunity.

In an example embodiment, the computing operation in FIG. 23 are used bya chatbot to predict sales. In particular, the chatbot providesrealistic (e.g. unbiased) and accurate sales pipeline numbers by accountrepresentative using use machine learning and data science algorithms.In another example aspect, the chatbot helps new sales people understandwhat is needed to be successful and create ideal hiring profiles.

Turning to FIG. 24, example executable instructions are provided forusing dynamic searches to affect the way certain data is outputted atthe user device.

Block 2401: While the user device plays audio of text, the user devicedetects a user's oral command to at least one of: repeat a portion oftext, search a portion of text, clarify a portion of text, comment on aportion of text, highlight or memorialize a portion of text, etc.

Block 2402: The user device or the data enablement platform, or both,executes the user's command.

Block 2403: The data enablement platform globally tallies the number oftimes the certain portion of text is acted upon by any and all users, orcertain highly ranked users, or both.

Block 2404: After a certain number of times has been counted, the dataenablement platform tags the certain portion of text.

Block 2405: When the certain portion of text, which is tagged, is beingplayed via audio means via an ancillary user device, the user deviceplays the audio text with emphasis (e.g. slower, louder, in a differenttone, in a different voice, etc.). In other words, the data enablementplatform has tagged the certain portion of the text and has performed anaudio transformation on the certain portion of text.

Turning to FIG. 25, example executable instructions are provided forprocessing voice data and background noise.

Block 2501: The user device or the OCD records audio data, includingvoice data and background noise.

Block 2502: The data enablement platform applies audio processing toseparate voice data from background noise.

Block 2503: The data enablement platform saves the voice data and thebackground noise as separate files and in association with each other.

Block 2504: The data enablement platform applies machine learning toanalyze voice data for: text; meaning; emotion; culture; language;health state of user; etc.

Block 2505: The data enablement platform applies machine learning toanalyze background noise for: environment, current activity engaged byuser, etc.

Block 2506: The data enablement platform applies machine learning todetermine correlations between features extracted from voice data andfeatures extracted from background noise.

In this way, information about the user can be more accuratelydetermined, such as their behavior and their surroundings. This in turncan be used provide sales opportunities that are better customized tothe user.

In an example embodiment, the user device, including and not limited tothe OCD, includes an onboard voice synthesizer to generate synthesizedvoices. Turning to FIG. 26, the onboard voice synthesizer is a DigitalSignal Processing (DSP) based system that resides on the user device. Itincludes one or more voice libraries. It also includes a text processor,an assembler, a linker module, a simulator, a loader, a DSP acceleratormodule which is managed by a hardware resources manager, and a voiceacquisition and synthesis module (e.g. an analog/digital converter anddigital/analog converter). The voice acquisition and synthesis module isin data communication with a microphone and an audio speaker.

FIG. 27 shows an example subset of components on a user device, whichincludes a DSP board/chip, an ADDA2 board/chip, a local bus of the DSPboard, a host bus, and a CPU of the smart device. These components, forexample, support the software architecture shown in FIG. 26.

It will be appreciated that different software and componentarchitectures (i.e. different from the example architectures shown inFIGS. 26 and 27) in a user device can be used to facilitate outputtingsynthesized voice data.

Turning to FIG. 28, example executable instructions are provided forbuilding a voice library.

Block 2801: The data enablement platform searches for media content thatincludes voice data about a given person (e.g. interviews,documentaries, self-posted content, etc.). Example data formats of mediacontent with voice data include videos and audio-only media.

Block 2802: The data enablement platform processes the media content toingest the voice data.

Block 2803: The data enablement platform decomposes the voice data intoaudio voice attributes of the given person. Examples of audio voiceattributes include frequency, amplitude, timbre, vowel duration, peakvocal sound pressure level (SPL), continuity of phonation, tremor, pitchvariability, loudness variability, tempo, speech rate, etc.

Block 2804: The data enablement platform generates a mapping of word tovoice attributes based on the recorded words.

Block 2805: The data enablement platform generates a mapping of syllableto voice attributes.

Block 2806: The data enablement platform constructs a synthesizedmapping between any word to voice attributes for the given person.

Block 2807: The data enablement platform generates a voice library forthe given person based on synthesized mapping.

Block 2808: The data enablement platform associates the voice librarywith the given person.

Block 2809: The user device that belongs to the user receives the voicelibrary of the given person.

Block 2810: The user device of locally stores the voice library inmemory. For example, the system wirelessly flashes the DSP chip so thatthe voice library of the given person is stored in RAM on the smartdevice (block 2811). This data can also be stored in some other manneron the user device.

FIG. 29 shows an example of memory devices 2901 on a user device. Thememory devices include faster access memory 2902 and slower accessmemory 2903. In one example embodiment, the faster access memory is RAMand the slower access memory is ROM. Other combinations of faster andslower memory devices can be used in alternative to RAM and ROM.

The faster access memory 2902 has stored on it, amongst other things, alibrary of frequently asked questions (FAQs) and frequent statements(FSs), and corresponding responses to these FAQs and FSs. The fasteraccess memory also has stored on it voice libraries of persons whointeract with the user, and a frequently accessed content libraries.These frequently accessed content libraries include multimedia. Theinformation or content stored in memory 2902 provides local, edge, fast“hot” reacting content that is frequently needed, so that there is noneed to go to the data enablement platform for same known-known data.

The slower access memory 2903 includes, amongst other things: datascience modules, collectors modules, communication modules, other voicelibraries, content libraries, and memories databases. The information orcontent stored in memory 2903 provides local, edge, fast “medium”reacting content that is needed, but not as frequently or immediately,so that there is no need to go to the data enablement platform for sameknown-known data.

Another data module called the cloud-based access module 2903 a allowsfor the user device to interact with the data enablement platform toaccess content libraries. This is also called cloud “cold” reactingcontent that is relatively less frequently used.

Block 2904: The user device detects a user has asked a FAQ or said a FS.

Block 2905: The user device accesses the faster access memory 2902 andidentifies an appropriate voice library for the asked FAQ or the saidFS.

Block 2906: The user device accesses the faster access memory 2902 andidentifies the appropriate response (e.g. audio, visual, text, etc.) tothe asked FAQ or the said FS.

Block 2907: The user device outputs audio or visual (or both) data usingthe identified appropriate response and the identified voice library. Inthis way, responses to FAQs and FSs occur very quickly, or even in realtime, so provide a conversation like experience.

Turning to FIG. 30, another example set of executable instructions areexecuted by the smart device of the patient.

Block 3001: The user device detects the person has asked a question orsaid a statement that is not a FAQ/FS.

Block 3002: The user device provides an immediate response using apredetermined voice library. For example, the smart device says “Let methink about it” or “Hmmm”. This response, for example, is preloaded intothe faster access memory 2902 for immediate retrieval.

Block 3003: The user device conducts one or more of the following toobtain a response: local data science, local search, external datascience, and external search. This operation, for example, includesaccessing the slower access memory 2903.

Block 3004: The user device identifies an appropriate voice library foroutputting the obtained response.

Block 3005: The user device outputs audio or visual (or both) data usingthe obtained response and identified voice library.

In this way, more complex algorithms are computed locally on the userdevice, either in part or in whole, while still providing an immediateresponse.

FIGS. 31 and 32 show another example embodiment of executableinstructions executed by a user device of a user. If an answer to auser's question or statement is not known, then the user deviceinitiates a message or communication session with a computing devicebelonging to a relevant contact of the user (e.g. a co-worker, acolleague, a friend, a client, a family member, a service provider, acontractor, etc.).

Block 3101: The user device detects that the user has asked a questionor said a statement that is not a FAQ/FS.

Block 3102: The user device provides an immediate response using apredetermined voice library. For example, the smart device accesses thefaster access memory 2902.

Block 3103: The user device identifies that one or more contacts arerequired to provide an appropriate response. For example, the userdevice accesses the slower access memory 3103 to obtain thisinformation.

Block 3104: The user device identifies an appropriate voice library foroutputting obtained response. For example, the user device accesses theslower access memory 3103 to obtain this information.

Block 3105: The user device outputs audio or visual (or both) data usingthe obtained response and identified voice library. For example, thesmart device says: “I will find out for you” or “I need to look upsomething and will get back to you”.

Block 3106: The user device generates and transmits message(s) toappropriate contact(s).

The one or more user devices of the contact then receive responses fromthe contacts. For example, the contact receives a text message, phonecall, video call, etc. in relation to the message from the user deviceof the patient, and

Block 3107: The user device receives response(s) from appropriatecontact(s).

Block 3108: The user device generates appropriate response based onreceived response(s) from appropriate contact(s).

Block 3109: The user device identifies the appropriate voice library foroutputting the appropriate response.

Block 3110: The user device outputs audio or visual (or both) data usingthe appropriate response and identified voice library.

In this way, the response from the one or more contacts are relayed backto the user device of the user.

Turning to FIG. 33, example executable instructions are provided foroutputting media content that includes synthesized voice content.

For example, a user asks “Tell me about Tesla's car production”. Thedata enablement application identifies that Elon Musk is a relatedauthority on this topic, finds the related content (e.g. text content,audio, video, etc.), and uses Elon Musk's synthesized voice to explainTesla's car production. For example, a chat bot using Elon Musk'ssynthesized voice says “Hello, I'm Elon Musk. Tesla's car manufacturingplants are located in . . . ”.

In another example, a user asks “What does Bill Gates know aboutAlzheimers?”. The data enablement application does a search for content(e.g. text content, audio, video, etc.) of Bill Gates in relation toAlzheimer disease, and uses Bill Gate's synthesized voice to explain hisinvolvement with Alzheimer disease. For example, a chat bot using BillGate's synthesized voice says “Hello, I'm Bill Gates. I am involved infunding research for detecting Alzheimer disease . . . ”.

In a first example embodiment in FIG. 33, the process starts with block3301.

Block 3301: Receive query about a topic (e.g. voice query)

Block 3302: Identify a given person who is an authority, expert, leaderetc. on the topic

Block 3303: Search and obtain text quotes, text articles, textinformation in relation to topic and/or said by the given person

Block 3304: Obtain voice library of the given person

Block 3305: Generate media content with at least audio content,including synthesized voice of person saying the obtained text data

Block 3306: Output the generated media content

In a second example embodiment, the process starts at block 3307 andcontinues from block 3307 to block 3303, then block 3304 and so forth.

Block 3307: Receive query about a given person and a topic (e.g. voicequery)

In an example aspect of block 3305, the data enablement platformcombines the synthesized voice data with recorded voice data, video,images, graphs, etc. (block 3308). In other words, the generated mediacontent includes multiple types of media.

Turning to FIG. 34, an example embodiment is provided for a user toinitiate a chat bot (e.g. a synthesized voice bot) of a given person ina given conversation mode. A conversation mode herein refers to a set ofparameters that affect the voice attributes and type of response orquestions that are used with a given voice bot. For example, a voice botof a Bill Gates can be used with a first conversation mode in oneexample implementation; the voice bot of Bill Gates can be used with asecond conversation mode is another implementation; a voice bot of ElonMusk can be used with the first conversation mode in anotherimplementation; and the voice bot of Elon Musk can be used with thesecond conversation mode in another implementation. In other words,different voice bots can be paired with different conversation modes,and vice versa.

In an example embodiment, a conversation mode includes one or more thefollowing parameters: tone; frequency (e.g. also called timbre);loudness; rate at which a word or phrase is said (e.g. also calledtempo); phonetic pronunciation; lexicon (e.g. choice of words); syntax(e.g. choice of sentence structure); articulation (e.g. clarity ofpronunciation); rhythm (e.g. patterns of long and short syllables);melody (e.g. ups and downs in voice); phrases; questions; and amount ofdetail given in a question or a statement. In an example embodiment,multiple conversation libraries store the parameters that define eachconversation mode.

In FIG. 34, the data enablement system builds or obtains variouslibraries, as per blocks 3401, 3402, 3403.

At block 3401, the data enablement system builds or obtains a voicelibrary for a given person.

At block 3402, the data enablement system builds or obtains topiclibraries associated with the given person by searching articles,interviews, social media, business/industry websites, videos, audiointerviews, press releases, etc. Non-limiting examples of topics includecompany, industry, business, product, services, technologies, personal,other people, etc.

Block 3402, the data enablement system builds or obtains conversationlibraries that respectively correspond to different parameters ofdifferent conversation modes. Non-limiting examples of conversationlibraries include one or more of: Relaxed discussion library;Introductory sales pitch library; Detailed sales pitch library; Dealclosing library; Interview/Research library; Debate library; andEncouragement library.

It will be appreciated that these libraries are established beforeimplementing the process in blocks 3404 to 3410.

Block 3404: Receive input to activate a voice bot of the given person ina given conversation mode.

Block 3405: Access/load the voice library of the given person, the givenconversation library, and one or more topic libraries associated withthe given person.

Block 3406: In an example embodiment, the data enablement platformpre-generate or pre-loads (or both) common responses or common questions(or both) associated with the given person or the given conversationmode (or both). For example, this can be pre-loaded into the user devicefor local access, or can be pre-loaded in a virtual machine on the dataenablement platform that is interacting with the user device.

Block 3407: Receive voice data from user. For example, the user says astatement or asks a question.

Block 3408: Analyze the sentiment of the voice data. Analyze the emotionof the voice data.

Block 3409: Generate/obtain statement response and/or question response.The statement response or the question response (or both) is outputtedin an intermediate form of text and corresponding voice parameters. Thisis determined by the given conversation library, the given topiclibrary, and the given voice library.

Block 3410: Output the generated response in the synthesized voice ofthe given person.

For example, using the above process, a person can activate a voice botof Bill Gates in a relaxed discussion conversation mode, and input thelanguage data “Hi Bill, what do you think about vaccines?”. A chat botusing Bill Gate's synthesized voice will then engage the user in adiscussion about vaccines, based on data extracted from various datasources involving Bill Gates and vaccines. Bill Gate's synthesized voicewill sound informative, relaxed and will also ask the user questions.

In another example, using the above process, a person can activate avoice bot of Elon Musk in an introductory sales pitch conversation mode,and input the language data “Hi Elon, there is this exciting newtechnology that can solve transportation problems.” A chat bot usingElon Musk's synthesized voice will then engage the user as a potentialinvestor and respond back with statements and questions like “What ispotential market capitalization?”; “How does your technology provide a10× improvement?”; and “It's an intriguing idea—tell me how this relatesto Tesla, SpaceX or the Boring Company.” Elon Musk's synthesized voicewill sound inquisitive and critical, and the syntax content of the chatbot's responses will be inquisitive and critical.

Other example features of the devices, systems and the methods areprovided below.

In an example embodiment, the devices, systems and the methods describedherein help sales people and sales managers to more accurately screen,evaluate and value early stage sales opportunities.

In an example embodiment, the devices, systems and the methods describedherein autonomously and updates information in real-time pertaining toearly sales opportunities, including but not limited to: people andrelationships related to the early stage sales opportunity; peers;friends; employees throughout organizations; salespersons organization;buy side people and organization; acquaintances; competition;competitor's recently released products and services; pricing and costrelated to competitive products and services; substitute solutions;latest industry news; orthogonal news that could change the opportunityevaluation; financial news; global regional news; and governmentregulations and laws.

In an example embodiment, the devices, systems and the methods describedherein autonomously capture early stage sales opportunities andautonomously update these opportunities through detailed information.This detailed information, for example, is also herein referred to asclose-to-“perfect information”, or “perfect information”.

In an example embodiment, the devices, systems and the methods describedherein autonomously predict and recommend what prioritized salesopportunities and prioritized tasks a salesperson should work, day byday, in order to achieve certain business metrics and goals.

In an example embodiment, the devices, systems and the methods describedherein autonomously predicts and recommends which sales leads should beassigned to a specific salesperson based on a salespersons' experience,knowledge, historical track record, to name a few, in order to increasesales opportunity closure rate.

In an example embodiment, the devices, systems and the methods describedherein autonomously evaluate salespeople.

In an example embodiment, the devices, systems and the methods describedherein autonomously evaluate marketing efforts that provide early stagesales opportunities.

In an example embodiment, the system described herein includes machinelearning software intelligence residing on a salesperson smartphone orthe OCD, or both. The data enablement platform includes machine learningsoftware that recognizes behaviors and patterns, and predicts behaviorsand patterns, within and outside the salesperson's organization. Thedata enablement platform uses these recognized data features andpredicted data features to make intelligent recommendations within thesalesperson's organization.

In an example embodiment, the machine learning includes data sciencealgorithms, and further includes co-computing alongside humaninteractions. In other words, human interactions with user devices, OCDsand other internal and external computing systems are used as inputsthat are ingested by the data enablement platform. This helps to providerelevant results for the salesperson, salespersons' manager, andexecutives.

In an example embodiment, the data enablement platform performsautonomous actions on the salesperson's smartphone or OCD, or both, andthe salesperson's organizational systems and apps. For example, theseautonomous actions include making recommendations, automating salespeople and sales manager tasks, and autonomously performing researchthat can impact an opportunity.

In an example embodiment, the data enablement platform ingests data fromN number external data sources (e.g. global news, blogs, forums, socialsites, industry blogs, financial sites, 3rd party proprietary sites andsystems, etc.).

In an example embodiment, the data enablement platform ingests data fromN number internal data sources within the salesperson's organization(CRM, ERP, HR, internal websites, proprietary systems, etc.).

In an example embodiment, the data enablement platform applies STRIPAdata science including but not limited to algorithms, graph database,machine learning, AI, etc, against the internal and external datasources to surface, trend, infer, predict, and act. In particular,STRIPA computing operations are applied to existing, new, and changinginformation pertaining to an opportunity and generates a machine-learnedbased opportunity score.

In an example embodiment, the data enablement platform applies STRIPAdata science during computer interactions with the salespeople, salesmanager, and executives using AI based voice conversations. Thesecomputations accelerate and enable a computing system to captureopportunity information, in an oral fashion (e.g. via microphonedevices). In an example implementation, the resulting data intelligenceprovides a holistic view of an opportunity at different perspectives,such as from the perspectives of a salesperson, a sales manager, and anexecutive. These computations also learn ad hoc information from any oneor more of the salesperson, the sales manager and the executiveregarding an opportunity in progress.

In an example embodiment, the data enablement platform applies STRIPAdata science to, and during, interactions with the salespeople, salesmanagers, and executives using the AI based voice conversations toautonomously remind these people to take specific actions in order toincrease the opportunity (e.g. opportunity closure rate, opportunityvalue, etc.).

In an example embodiment, the data enablement platform applies STRIPAdata science to interact with the salespeople, sales managers, andexecutives using AI based voice conversations to create ad hocopportunities that are too early to put into a traditional CRM systembut that can lead to material opportunities automatically and withouthuman intervention.

In an example embodiment, the data enablement platform applies STRIPAdata science to interact with the salespeople, sales managers, andexecutives using AI based voice conversations to remind sales people toorally provide certain information in order to move an opportunity fromone sales step to another sales step, for compliance purposes (e.g. asper internal policy).

In an example embodiment, the data enablement platform applies STRIPAdata science to interact with the salespeople, sales managers, andexecutives using AI based voice conversations to update the salespersonwith the latest news about an opportunity including but not limited to anew employee that has personal relationships at the opportunity, a newexecutive at the opportunity, a competitor's product or service thatmight impact the opportunity, etc.

In an example embodiment, the data enablement platform applies STRIPAdata science to interact with the salespeople, sales managers, andexecutives using AI based voice conversations to follow company specificsales process compliance steps and rules.

In an example embodiment, the data enablement platform applies STRIPAdata science to interact with the salespeople, sales managers, andexecutives using AI based voice conversations to learn each person'sbehaviors to consequently make recommendations to increase/optimize theeffectiveness of a salesperson or a sales manager, or both.

In an example embodiment, the data enablement platform applies STRIPAdata science to learn and autonomously give marketing leads to certainsalespeople who have historically demonstrated (via STRIPA, machinelearning, AI, etc.) a higher probability of closing an opportunity in acertain industry, function, company or solution characteristics, etc.

In an example embodiment, the data enablement platform uses some or allof the aforementioned operations and features in order to customize thesolution for a sales organization. In other example embodiment, the dataenablement platform is applied to other industries.

Additional general example embodiments and aspects are described below.

In an example embodiment, an oral computing device is provided, whichincludes a housing that holds at least: a memory device that storesthereon a data enablement application that includes a conversational botand a user account ID, the user account ID used to access privatedatabases; a microphone that is configured to record a user's spokenwords as audio data; a processor configured to use the conversationalbot to identify contextual data associated with the audio data, thecontextual data including a current mode of the data enablementapplication and the user account ID; a data communication deviceconfigured to transmit the audio data and the contextual data via a datanetwork and, in response, receive response data, wherein the responsedata is a function of data obtained from the private database and dataobtained from external databases; and an audio speaker that iscontrolled by the processor to output the response data as audioresponse data.

In an example aspect, the oral computing device is a wearable device todynamically interact with the data. For example, the wearable deviceincludes inertial measurement sensors. In another example, the wearabledevice is a smart watch. In another example, the wearable device is aheadset. In another example, the wearable device projects images toprovide augmented reality.

In another example aspect, the oral computing device projects lightimages on surrounding surfaces to provide augmented reality of virtualreality. In another example aspect, the oral computing device is in dataconnection with other devices that projects light images to provideaugmented reality or virtual reality in a room. In effect, people thatare physically present in the room, or virtual people being displayed bythe projected light images, simultaneously interact and collaborate witheach other.

In an example aspect, the oral computing device includes a graphicsprocessing unit (GPU) that exchanges data with the processor, the GPUconfigured to pre-process the audio data using parallel threadedcomputations to extract data features, and the data communication devicetransmits the extracted data features in association with the contextualdata and the audio data.

In an example embodiment, the oral computing device is a user device 102or the specific embodiment of the OCD 301.

In another general example embodiment, a data enablement system (alsoherein called the data enablement platform) is provided that includescloud computing servers that ingest audio data originating from one ormore user devices, the audio data comprising at least oral conversationof one or more users, and the cloud computing servers configured toapply machine learning computations to extract at least content andsentiment data features. The data enablement system also includes datascience servers in data communication with the cloud computing servers,internal applications and databases, and an external artificialintelligence computing platform. The data science servers also include alibrary of data science algorithms used to process the content andsentiment features using internal data obtained from the internalapplications and databases, and external data obtained from the externalartificial intelligence computing platform. The data science serversoutput response data to the cloud computing servers, the response databeing in response to the audio data. Subsequently, the cloud computingservers format the response data into an audio data format playable by agiven user device, and transmit the formatted response data.

In another general example embodiment, a speech computing devicecomprises: a memory device that stores thereon at least a dataenablement application that comprises multiple modules that correspondto different modes, a conversational bot and one or more synthesizedvoice libraries, wherein each of the one or more synthesized voicelibraries comprise voice parameter features of one or more correspondingpeople; an input sensor that is configured to record a user's input asspeech data; a processor configured to use the conversational bot toidentify contextual data associated with the speech data, the contextualdata including a current mode corresponding to a currently activatedmodule of the data enablement application; and a data communicationdevice configured to transmit the audio data and the contextual data viaa data network and, in response, receive response data, wherein theresponse data comprises an indication of a given synthesized voicelibrary, text data, and the current mode; the processor furtherconfigured to use the conversational bot to generate an audio responsefrom the given synthesized voice library and the text data, and topropagate the text data to one or more other modules that are currentlyinactive; and an audio speaker that is controlled by the processor tooutput the audio response.

In an example aspect, the currently activated module is a meeting notesmodule; the speech data comprises a topic; and the text data comprisesdata in relation to the topic.

In a further example aspect, the computing device detects at least oneof a pause or an end of a sentence in the speech data and then outputsthe audio response.

In a further example aspect, the speech data and the text data is addedto a meeting notes file.

In a further example aspect, the data communication device is incommunication with at least one other user device, and the computingdevice further transmits additional data about the topic to the otheruser device within a same given time period as outputting the audioresponse.

In a further example aspect, the input sensor obtains public speechdata, the computing device further receives private meeting notes, andthe computing device further generates meeting notes that comprise theprivates meeting notes and public notes derived from the public speechdata and the text data in the response data.

In a further example aspect, the private notes and the public notes areorganized by time of creation.

In a further example aspect, the data communication device is incommunication with at least a silent communication device to obtainprivate speech data; and the computing device further generates theprivate meeting notes from the private speech data.

In a further example aspect, the computing system further comprises avisual display device, and the response data further includes visualdata that is outputted with the audio response.

In a further example aspect, the visual display device is a projector.

In a further example aspect, the speech data comprises a topic; and thetext data comprises a summarization of multiple news articles inrelation to the topic.

In a further example aspect, the currently activated module is anintroductions module associated with a social network platform of theuser; the speech data comprises a topic or an entity; and the text datacomprises a list of names obtained from the social network platform thatare related to the topic or the entity.

In a further example aspect, the multiple modules comprise a to-do listmodule, an opportunities module, an introductions module, a meetingnotes module and a new module; and wherein the currently activated ofthe multiple modules propagates the text data to at least two or more ofthe other ones of the multiple modules.

In a further example aspect, the memory device further storesconversation libraries that include one or more parameters that are usedby the conversational bot to affect the audio response; and theparameters comprise one or more of: tone; frequency; loudness; rate atwhich a word or phrase is said; phonetic pronunciation; lexicon; syntax;articulation; rhythm; melody; phrases; and questions.

In a further example aspect, the speech data comprises a topic; and theindication of the given synthesized voice library is associated with aperson that is an authority or an expert of the topic.

In a further example aspect, the speech data comprises a topic and aname of a person; the indication of the given synthesized voice libraryis associated with the person; and the text data is in relation to boththe topic and the person.

In a further example aspect, the computing device includes a graphicsprocessing unit (GPU) that exchanges data with the processor, the GPUconfigured to pre-process the audio data using parallel threadedcomputations to extract data features, and the data communication devicetransmits the extracted data features in association with the contextualdata and the audio data.

It will be appreciated that any module or component exemplified hereinthat executes instructions may include or otherwise have access tocomputer readable media such as storage media, computer storage media,or data storage devices (removable and/or non-removable) such as, forexample, magnetic disks, optical disks, or tape. Computer storage mediamay include volatile and non-volatile, removable and non-removable mediaimplemented in any method or technology for storage of information, suchas computer readable instructions, data structures, program modules, orother data. Examples of computer storage media include RAM, ROM, EEPROM,flash memory or other memory technology, CD-ROM, digital versatile disks(DVD) or other optical storage, magnetic cassettes, magnetic tape,magnetic disk storage or other magnetic storage devices, or any othermedium which can be used to store the desired information and which canbe accessed by an application, module, or both. Any such computerstorage media may be part of the servers or computing devices oraccessible or connectable thereto. Any application or module hereindescribed may be implemented using computer readable/executableinstructions that may be stored or otherwise held by such computerreadable media.

It will be appreciated that different features of the exampleembodiments of the system and methods, as described herein, may becombined with each other in different ways. In other words, differentdevices, modules, operations, functionality and components may be usedtogether according to other example embodiments, although notspecifically stated.

The steps or operations in the flow diagrams described herein are justfor example. There may be many variations to these steps or operationsaccording to the principles described herein. For instance, the stepsmay be performed in a differing order, or steps may be added, deleted,or modified.

The GUIs and screen shots described herein are just for example. Theremay be variations to the graphical and interactive elements according tothe principles described herein. For example, such elements can bepositioned in different places, or added, deleted, or modified.

It will also be appreciated that the examples and corresponding systemdiagrams used herein are for illustrative purposes only. Differentconfigurations and terminology can be used without departing from theprinciples expressed herein. For instance, components and modules can beadded, deleted, modified, or arranged with differing connections withoutdeparting from these principles.

Although the above has been described with reference to certain specificembodiments, various modifications thereof will be apparent to thoseskilled in the art without departing from the scope of the claimsappended hereto.

1. A speech computing device comprising a housing that holds at least: amemory device that stores thereon at least a data enablement applicationthat comprises multiple modules that correspond to different modes, aconversational bot and one or more synthesized voice libraries, whereineach of the one or more synthesized voice libraries comprise voiceparameter features of one or more corresponding people; an input sensorthat is configured to record a user's input as speech data; a processorconfigured to use the conversational bot to identify contextual dataassociated with the speech data, the contextual data including a currentmode corresponding to a currently activated module of the dataenablement application; a data communication device configured totransmit the audio data and the contextual data via a data network and,in response, receive response data, wherein the response data comprisesan indication of a given synthesized voice library, text data, and thecurrent mode; the processor further configured to use the conversationalbot to generate an audio response from the given synthesized voicelibrary and the text data, and to propagate the text data to one or moreother modules that are currently inactive; and an audio speaker that iscontrolled by the processor to output the audio response.
 2. Thecomputing device of claim 1 wherein the currently activated module is ameeting notes module; the speech data comprises a topic; and the textdata comprises data in relation to the topic.
 3. The computing device ofclaim 2 wherein the computing device detects at least one of a pause oran end of a sentence in the speech data and then outputs the audioresponse.
 4. The computing device of claim 2 wherein the speech data andthe text data is added to a meeting notes file.
 5. The computing deviceof claim 2 wherein the data communication device is communication withat least one other user device, and the computing device furthertransmits additional data about the topic to the other user devicewithin a same given time period as outputting the audio response.
 6. Thecomputing device of claim 2 wherein the input sensor obtains publicspeech data, the computing device further receives private meetingnotes, and the computing device further generates meeting notes thatcomprise the privates meeting notes and public notes derived from thepublic speech data and the text data in the response data.
 7. Thecomputing device of claim 6 wherein the private notes and the publicnotes are organized by time of creation.
 8. The computing device ofclaim 6 wherein the data communication device is in communication withat least a silent communication device to obtain private speech data;and the computing device further generates the private meeting notesfrom the private speech data.
 9. The computing device of claim 1 furthercomprising a visual display device, and the response data furtherincludes visual data that is outputted with the audio response.
 10. Thecomputing device of claim 9 wherein the visual display device is aprojector.
 11. The computing device of claim 1 wherein the speech datacomprises a topic; and the text data comprises a summarization ofmultiple news articles in relation to the topic.
 12. The computingdevice of claim 1 wherein the currently activated module is anintroductions module associated with a social network platform of theuser; the speech data comprises a topic or an entity; and the text datacomprises a list of names obtained from the social network platform thatare related to the topic or the entity.
 13. The computing device ofclaim 1 wherein the multiple modules comprise a to-do list module, anopportunities module, an introductions module, a meeting notes moduleand a new module; and wherein the currently activated of the multiplemodules propagates the text data to at least two or more of the otherones of the multiple modules.
 14. The computing device of claim 1wherein the memory device further stores conversation libraries thatinclude one or more parameters that are used by the conversational botto affect the audio response; and the parameters comprise one or moreof: tone; frequency; loudness; rate at which a word or phrase is said;phonetic pronunciation; lexicon; syntax; articulation; rhythm; melody;phrases; and questions.
 15. The computing device of claim 1 wherein thespeech data comprises a topic; and the indication of the givensynthesized voice library is associated with a person that is anauthority or an expert of the topic.
 16. The computing device of claim 1wherein the speech data comprises a topic and a name of a person; theindication of the given synthesized voice library is associated with theperson; and the text data is in relation to both the topic and theperson.
 17. The computing device of claim 1 further comprising agraphics processing unit (GPU) that exchanges data with the processor,the GPU configured to pre-process the audio data using parallel threadedcomputations to extract data features, and the data communication devicetransmits the extracted data features in association with the contextualdata and the audio data.
 18. (canceled)